Robots are more and more widely used in work scenes, but few studies have discussed the influence of robots on front-line employees. This study aims to investigate whether the transparency and anthropomorphism of robots affect employees’ acceptance of robots and its influencing mechanism from the perspective of social identity. We test our hypotheses in two studies. Study 1 examined the main effect of robot transparency on employee’s acceptance and the mediating role of human–robot trust, while Study 2 further examined the main and mediating effects, as well as the moderating role of robot anthropomorphism. The findings revealed that that robot transparency positively affects employee’s acceptance, cognition-based trust mediates the relationship, and the mediating effect of affect-based trust is not significant. Robot anthropomorphism moderates the relationship between transparency and cognition-based trust, it also moderates the mediating effect of cognition-based trust between transparency and employee’s acceptance.

Immersive visualization has become an important tool for discovering hidden patterns and obtaining insights from data. Target acquisition in immersive visualization is a fundamental step in visual analysis. However, limited visual encoding attributes and the presence of stacking and occlusion in immersive environments pose challenges in discovering valuable targets and making unambiguous selections. In this paper, we present ClayVolume, an interactive system designed for immersive visualization. It comprises metaphorical tools for customizing regions of interest (ROIs) and multiple views that serve as interactive and analytical mediums. ClayVolume empowers analysts to efficiently acquire valuable targets through a progressive refinement of interactive methods, enabling further extraction of insights. We evaluate ClayVolume in the scenario of immersive visualization of network data and perform a comparative analysis of its performance against other techniques in target selection tasks. The results indicate that ClayVolume enables flexible target selection in immersive visualization and provides fast target discovery and localization capabilities. • A selection tool for defining ROIs in immersive visualization with depth awareness. • A navigation tool using WiM technology, featuring destination previews for accuracy. • A multi-view tool for data awareness, optimizing target acquisition with spatial cues.

The time since deposition (TSD) of a bloodstain, i.e., the time of a bloodstain formation is an essential piece of biological evidence in crime scene investigation. The practical usage of some existing microscopic methods (e.g., spectroscopy or RNA analysis technology) is limited, as their performance strongly relies on high-end instrumentation and/or rigorous laboratory conditions. This paper presents a practically applicable deep learning-based method (i.e., BloodNet) for efficient, accurate, and costless TSD inference from a macroscopic view, i.e., by using easily accessible bloodstain photos. To this end, we established a benchmark database containing around 50,000 photos of bloodstains with varying TSDs. Capitalizing on such a large-scale database, BloodNet adopted attention mechanisms to learn from relatively high-resolution input images the localized fine-grained feature representations that were highly discriminative between different TSD periods. Also, the visual analysis of the learned deep networks based on the Smooth Grad-CAM tool demonstrated that our BloodNet can stably capture the unique local patterns of bloodstains with specific TSDs, suggesting the efficacy of the utilized attention mechanism in learning fine-grained representations for TSD inference. As a paired study for BloodNet, we further conducted a microscopic analysis using Raman spectroscopic data and a machine learning method based on Bayesian optimization. Although the experimental results show that such a new microscopic-level approach outperformed the state-of-the-art by a large margin, its inference accuracy is significantly lower than BloodNet, which further justifies the efficacy of deep learning techniques in the challenging task of bloodstain TSD inference. Our code is publically accessible via https://github.com/shenxiaochenn/BloodNet. Our datasets and pre-trained models can be freely accessed via https://figshare.com/articles/dataset/21291825.

In-vehicle human-machine interface (HMI) plays a significant role in accomplishing effective interactions between driving automation systems and drivers, especially during the transition of control. For this reason, different in-vehicle HMIs have been designed to convey additional semantic information from the driving automation systems to the drivers to realize safer, smoother, and better control transitions. This review summarizes and analyses 86 previously published studies that researched the effects of additional semantic information delivered through in-vehicle HMIs just before, during and right after takeover request (TOR). The additional semantic information mentioned in this review refer to the information beyond simple alerts to not only gain drivers’ attention but also additionally communicate contextual content and explanation to the drivers regarding its own purpose. In this review, the additional semantic information are categorized according to their purposes and effects into three aspects: mode awareness enhancement, situation awareness enhancement, and takeover maneuver assistance. The specificities and the corresponding concerns when applying additional semantic information to in-vehicle HMIs have been detailed analyzed throughout the entire article. Further suggestions are proposed for what should be carefully considered when adding additional information for better takeover. Prospects into future in-vehicle HMI possibilities are also raised that could be applied in both academic research and industry.

Although latent hazard notification for highly automated driving is expected to enhance traffic safety, its practical effects have yet to be verified. This study systemically investigated the expected safety benefits and driver behavioral adaptation based on structural equation modeling. First, we developed a notification system to inform drivers of latent hazards with auditory alerts and conducted a driving simulation experiment involving eyes-off-road situations. To test the system, we adopted two types of events (i.e., the collision avoidance function working or failure) in which latent hazards transform into immediate risks. Then, a measurement model was developed to evaluate driver trust, driver attention, and traffic safety. Subsequently, we examined the corresponding causal relationships. On the one hand, latent hazard notification significantly improves driver attention (i.e., more fixations on latent hazards, less engagement in non-driving-related tasks, and faster notice of immediate risks), which significantly enhances traffic safety. On the other hand, latent hazard notification significantly increases driver trust, which lowers driver attention and consequently impairs traffic safety. This causality reveals driver behavioral adaptation, although driver trust does not directly affect traffic safety. Overall, we find that latent hazard notification for highly automated driving can improve traffic safety, but the consequent driver behavioral adaptation impairs 15.12% of the expected safety benefits.

For highly automated vehicles, effective take-over performance measures are essential for establishing quantitative take-over models and exploring approaches to improve take-over performance. However, there is a lack of comprehensive take-over performance measures that suitably combine multiple objective metrics based on an average evaluation from human drivers. In this study, we proposed a human-centered comprehensive measure of take-over performance (HCMTP). There are four main building blocks for the HCMTP. First, we adopted sparse principal component analysis to identify the main aspects of take-over performance based on multiple original objective take-over performance metrics. Second, we developed a scale of take-over performance assessment to obtain drivers’ original subjective self-assessments of take-over performance. Third, we established nonlinear individual mapping functions to acquire different drivers’ evaluation criteria for take-over performance. Fourth, we proposed a relabeling algorithm to obtain drivers’ average evaluation of take-over performance. To verify the effectiveness of the HCMTP, we conducted a verification experiment involving 68 participants. The results indicate that the HCMTP is effective and able to reduce the interference of individual differences, stochasticity, and data imbalance. This study contributes to identifying the main aspects of take-over performance, systematically understanding how human drivers subjectively evaluate take-over performance, and evaluating drivers’ take-over performance comprehensively.

Immersive visualization utilizes virtual reality, mixed reality devices, and other interactive devices to create a novel visual environment that integrates multimodal perception and interaction. This technology has been maturing in recent years and has found broad applications in various fields. Based on the latest research advancements in visualization, this paper summarizes the state-of-the-art work in immersive visualization from the perspectives of multimodal perception and interaction in immersive environments, additionally discusses the current hardware foundations of immersive setups.By examining the design patterns and research approaches of previous immersive methods, the paper reveals the design factors for multimodal perception and interaction in current immersive environments. Furthermore, the challenges and development trends of immersive multimodal perception and interaction techniques are discussed, and potential areas of growth in immersive visualization design directions are explored.

As human-machine collaborative driving systems, highly automated driving vehicles require human drivers to take over when take-over requests are triggered. Extensive studies have shown that drivers’ take-over performance is affected by their fatigue state, traffic conditions, and the take-over time budget (TB). However, there is still a paucity of a systematic understanding of how these factors affect take-over performance, which prevents the implementation of adaptive take-over systems. This study establishes a highly accurate take-over performance prediction model to systematically explore the effects of these factors on take-over performance and to propose an adaptive TB adjustment strategy for highly automated driving vehicles. First, we propose metrics to evaluate drivers’ fatigue states and the relative positions of surrounding traffic. Second, a generalized additive model is established to predict take-over performance and accurately evaluate the influence of the aforementioned factors on take-over performance. Based on the model, we propose an adaptive adjustment strategy of the TB for take-over systems and demonstrate its effectiveness by a verification experiment. This study contributes to understanding the influence of drivers’ passive fatigue states, the relative positions of surrounding traffic, and the TB on drivers’ take-over performance as well as to the development of adaptive take-over systems for highly automated vehicles.

Based on the area between the curve of the membership function and the horizontal real axes, a new index, called the expansion center for fuzzy numbers is proposed. An intuitive and reasonable ranking method for fuzzy numbers based on their expansion center is also established. This new ranking method is useful in fuzzy decision making and fuzzy data mining.

Nonlinear integrals (NIs) are useful integration tools. It can get a set of virtual values by projecting original data onto a virtual space for classification purpose using NIs. The classical NIs implement projection along a line with respect to the features. But, in many cases, the linear projection cannot achieve good performance for classification or regression due to the limitation of the integrand. The linear function used for the integrand is just a special type of function with respect to the features. In this paper, we propose a nonlinear integrals with polynomial kernel (NIPK). A polynomial function with respect to the features is used as the integrand of NIs. It enables the projection to be along different types of curves to the virtual space so that the virtual values gotten by NIs can be better regularized and have higher separation power for classification. We use genetic algorithm to learn the fuzzy measures so that a larger solution space can be searched. To test the capability of the NIPK, we apply it to classification on several benchmark datasets and a bioinformatics project. Experiments show that there is evident improvement on performance for the NIPK compared to classical NIs. © 2011 Wiley Periodicals, Inc.

A new nonlinear multiregression model based on a pair of extreme nonlinear integrals, upper and lower nonlinear integrals with respect to signed fuzzy measure, is established in this paper. A data set with the predictive features and the relevant objective feature is required for estimating the regression coefficients. Owing to the nonadditivity of the model, a multiobjective optimization using genetic algorithm is adopted to search for the optimized solution in the regression problem. Applying such a nonlinear multiregression model, an interval prediction for the value of the objective feature can be made once a new observation of predictive features is available. We apply our model on synthetic data and weather problem. The results testify the performance of the multiregression based on upper and lower nonlinear integrals. © 2012 Wiley Periodicals, Inc.

As an aggregation tool in information fusion and data mining, the Choquet integral is generalized to allow the involved set function being fuzzy-valued. A calculation formula of such a Choquet integral is developed when the universal set is finite, such as the set of attributes in a database.

In this paper, the original Choquet integral is generalized as a Fuzzified Choquet Integral with a Fuzzy-valued Integrand (FCIFI), which supports a fuzzy-valued integrand and an integration result. The calculation of the FCIFI is established on the Choquet integral with an interval-valued integrand (CIII). The definitions, properties, and calculation algorithms of the CIII and the FCIFI are discussed and proposed in this paper. As a specific application scheme, we designed a CIII regression model for the regression problems involving interval-valued data. This CIII regression model has a self-learning ability through a double genetic algorithm. Finally, a daily temperature predictor based on the CIII regression model is discussed, where a series of experiments is implemented to validate the performance of the predictor by real weather records from the Hong Kong Observatory.

In this paper, we propose a new nonlinear classifier based on a generalized Choquet integral with signed fuzzy measures to enhance the classification power by capturing all possible interactions among two or more attributes. A special genetic algorithm is designed to implement this classification optimization with fast convergence. Instead of using a discrete misclassification rate, the objective function to be optimized in this research is a continuous Choquet distance with a penalty coefficient for misclassified points. The numerical experiment shows that the special genetic algorithm effectively solves the nonlinear classification problem and this nonlinear classifier accurately identifies classes.

As a fuzzification of the Choquet integral, the defuzzified choquet integral with fuzzy-valued integrand (DCIFI) takes a fuzzy-valued integrand and gives a crisp-valued integration result. In this paper, the DCIFI acts as a projection to project high-dimensional heterogeneous fuzzy data to one-dimensional crisp data to handle the classification problems involving different data forms, such as crisp data, interval values, fuzzy numbers, and linguistic variables, simultaneously. The nonadditivity of the signed fuzzy measure applied in the DCIFI can represent the interaction among the measurements of features towards the discrimination of classes. Values of the signed fuzzy measure in the DCIFI are considered to be unknown parameters which should be learned before the classifier is used to classify new data. We have implemented a genetic algorithm (GA)-based adaptive classifier-learning algorithm to optimally learn the signed fuzzy measure values and the classified boundaries simultaneously. The performance of our algorithm has been tested both on synthetic and real data. The experimental results are satisfactory and outperform those of existing methods, such as the fuzzy decision trees and the fuzzy-neuro networks.

Linear programming (LP) based models provide good solutions to classification problem especially when the data is linearly separable. The assumption of LP classification models is: the contributions from all attributes towards the classification model are the sum of contributions of each attribute. This assumption leads to a weakness of LP classification models when data is linearly inseparable. The concept of signed fuzzy measure is introduced and utilized in LP approach in order to enhance the classification power through capturing all possible interactions among any two or more attributes. The use of the Choquet integral with respect to a signed fuzzy measure on LP model is able to separate the data that is finearly inseparable.

Various types of integrals with respect to signed fuzzy measures on finite sets with cardinality n can be presented as corresponding rules for partitioning the integrand. The partition can be expressed as an n-dimensional vector, whereas the signed fuzzy measure is also an n-dimensional vector. Thus, the integration value is the inner product of these two vectors. Two pairs of extremes, the Lebesgue-like integral versus the Choquet integral and the upper integral versus the lower integral, are discussed in detail. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 1073–1092, 2006.

We develop an iterative algorithm for identifying approximate optimal scales for feature attributes of generalized Choquet integrals. Based on the scales, the optimal values of fuzzy measures are also obtained immediately using least square method. The new algorithm is significantly faster than previous approaches using evolutionary computing. The experimental results produced by the iterative algorithm are also better than those from genetic algorithms

This paper gives a new genetic algorithm for nonlinear multiregression based on generalized Choquet integrals with respect to signed fuzzy measures. Unlike the previous work where the values of the signed fuzzy measure are determined by random search in a genetic algorithm with other regression coefficients together; in this new algorithm, they are determined algebraically and, therefore, its complexity is much lower than before.

A new method based on nonlinear integral projections for classification is presented. The contribution rate of each combination of the feature attributes, including each singleton, toward the classification is represented by a fuzzy measure. The nonadditivity of the fuzzy measure reflects the interactions among the feature attributes. The weighted Choquet integral with respect to the fuzzy measure serves as an aggregation tool to project the feature space onto a real axis optimally according to an error criterion, and the classifying attribute is properly numerical analysed on the axis simultaneously making the classification simple. To implement the classification, we need to determine the unknown parameters, the values of fuzzy measure and the weight function. This can be done by running an adaptive genetic algorithm on the given training data. The new classifier is tested by recovering the preset parameters from a set of artificial training data generated from these parameters. It also performs well on several real-world data sets. Beyond discriminating classes, this method can also learn the scaling requirements and the respective importance indexes of the feature attributes as well as the relationships among them. A comprehensive discussion on the semantic and geometric meanings of the parameters is given. Moreover, we show how these parameters' values can be used for short-listing important feature attributes to reduce the complexity (dimensions) of the classification problem. Our new method also compares favorably with other methods on some well-known real-world benchmarks.

A nonlinear multiregression model is presented based on the generalized Choquet integral with respect to a signed fuzzy measure. In this model, the interaction among predictive attributes toward the objective attribute is depicted by a signed fuzzy measure. To guarantee the invariability of the multiregression under scale variation and translation of the attributes, a respective linear transformation with unknown coefficients is applied to each attribute. All of these coefficients and the values of the signed fuzzy measure are optimally determined as the regression coefficients by running an adaptive genetic algorithm based on given data.

This paper describes a novel knowledge discovery and data mining framework dealing with nonlinear interactions among domain attributes. Our network-based model provides an effective and efficient reasoning procedure to perform prediction and decision making. Unlike many existing paradigms based on linear models, the attribute relationship in our framework is represented by nonlinear nonnegative multiregressions based on the Choquet integral. This kind of multiregression is able to model a rich set of nonlinear interactions directly. Our framework involves two layers. The outer layer is a network structure consisting of network elements as its components, while the inner layer is concerned with a particular network element modeled by Choquet integrals. We develop a fast double optimization algorithm (FDOA) for learning the multiregression coefficients of a single network element. Using this local learning component and multiregression-residual-cost evolutionary programming (MRCEP), we propose a global learning algorithm, called MRCEP-FDOA, for discovering the network structures and their elements from databases. We have conducted a series of experiments to assess the effectiveness of our algorithm and investigate the performance under different parameter combinations, as well as sizes of the training data sets. The empirical results demonstrate that our framework can successfully discover the target network structure and the regression coefficients.

Multiregression is one of the most common approaches used to discover dependency pattern among attributes in a database. Nonadditive set functions have been applied to deal with the interactive predictive attributes involved, and some nonlinear integrals with respect to nonadditive set functions are employed to establish a nonlinear multiregression model describing the relation between the objective attribute and predictive attributes. The values of the nonadditive set function play a role of unknown regression coefficients in the model and are determined by an adaptive genetic algorithm from the data of predictive and objective attributes. Furthermore, such a model is now improved by a new numericalization technique such that the model can accommodate both categorical and continuous numerical attributes. The traditional dummy binary method dealing with the mixed type data can be regarded as a very special case of our model when there is no interaction among the predictive attributes and the Choquet integral is used. When running the algorithm, to avoid a premature during the evolutionary procedure, a technique of maintaining diversity in the population is adopted. A test example shows that the algorithm and the relevant program have a good reversibility for the data. © 2001 John Wiley & Sons, Inc.16: 949–962 (2001)

Due to some inherent interactions among diverse information sources, the classical weighted average method is not adequate for information fusion in many real problems. To describe the interactions, an intuitive and effective way is to use an appropriate nonadditive set function. Instead of the weighted average method, which is essentially the Lebesgue integral, we should thus use the Choquet integral or some other nonlinear integrals. To apply this alternative, more realistic approach to information fusion, we need to determine the nonadditive set function from given input-output data, viewing the nonlinear integral as a multi-input one-output system. In this paper, we employ an adaptive genetic algorithm to construct an approximate optimal nonnegative monotone set function from given input-output data in an environment with random perturbation. An example for diverse strengths of random perturbation is shown to demonstrate the efficiency of this algorithm. ©1999 John Wiley & Sons, Inc.

By using a non-additive set function to describe the interaction among variables, a nonlinear non-negative multi-regression is established based on the Choquet integral with respect to the set function. We generalize this nonlinear model and propose a novel formalism that provides an effective and efficient reasoning procedure to perform information fusion, decision making, and medical diagnoses. In the formalism, a network structure and a number of Choquet integrals are used to represent the relationships among variables. We propose a new algorithm to learn the network structure and the regression parameters of Choquet integrals from training examples in databases. The algorithm is based on the minimum description length (MDL) principle and evolutionary programming (EP). We conduct a series of experiments to demonstrate the performance of our algorithm and estimate the effectiveness of the MDL metric and the genetic operators. The empirical results illustrate that our algorithm can successfully discover the target network structure and the regression parameter.

We develop a nonlinear multi-regression model based on the Wang integral to describe a multi-input single-output system. In this model, in general, set function /spl mu/ is nonadditive. The nonadditivity of /spl mu/ describes the inherent interaction among the input attributes x/sub 1/, x/sub 2/, ..., x/sub n/. When the proper input-output data are available, by using the adaptive genetic algorithm shown in this paper, rather precise estimated values of parameter c, q, w and /spl mu/ of the regression model can be obtained. Thus, the multi-input single-output system can be used to make prediction. That is to say, when the values of input attributes x/sub 1/, X/sub 2/, ..., X/sub n/, are known, we can predict the output Y by calculating the nonlinear multi-regression.

A pair of new integrals, a PFB-integral and a PFA-integral, are introduced. Given a measurable space, these integrals are defined for nonnegative functions with respect to monotone set functions, and they are generalizations of both the Lebesgue integral and the fuzzy integral. Some primary properties of the PFB-integral and PFA-integral are discussed and an inequality between them is established in this paper.

To replace the traditional weighted average method, Choquet integrals or Sugeno integrals with respect to fuzzy measures are used to obtain a synthetic evaluation of a given object (or its quality, function, etc. respectively) with multi-attribute. Generally, it is not easy to determine fuzzy measures in real problems due to the subjectivity of human thinking. It is even much more difficult than determining weights in the weighted average method, because of the nonadditivity of fuzzy measures. This paper uses a neural network algorithm to optimize the inverse problem of synthetic evaluation, and thus to determine Sugeno measures by the Choquet integral and statistics of given data. Since the Choquet integral is a generalization of the weighted average method, this technology has a broad applicability in areas of multivariate analysis, decision making, pattern recognition, image and speech processing and expert systems. Copyright 1996 Elsevier Science Ltd.